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Pedestrian detection, as one of the most important modules of intelligent vehicles, is a challenging topic for researchers. In this paper, we propose a stereo-vision-based and shape-based approach for pedestrian and bicyclist detection. An efficient stereo system and an obstacle detection algorithm based on v-disparity map help us locate potential regions. Using the shape of the rigid part (upper body) of pedestrians and bicyclists, the matching criterion of partial Hausdorff distance, can efficiently detect them from front or back views. Our algorithm is tested off-line on a large mount of data, and the experiments show its realtime and reliable performance.